Anomaly Warning and Fault Detection in DC Pico-grid with enhanced k-Nearest Neighbours Technique

The k-nearest neighbours (kNN) algorithm, which is usually used for classification, is presented in this paper to detect faults and trigger anomaly warnings in a single sensor multiple loads dc pico-grid. Anomalies warning is getting more attention in the recent years as it can used as a trigger for predictive maintenance, which is preferred over repair work after a fault detection. On top of performing its usual duty of load classification in the circuit during normal operation, the kNN algorithm is enhanced with 3 additional techniques to set 3 anomaly criteria for the triggering of alarm when the extracted features of the test object exhibit abnormal behaviours. The experiment is set in a dc pico-grid as there is a growing interest and demand in dc loads. Experiments with various anomalies show that the proposed enhanced algorithm can effectively detect anomalies and faults.